Ownership Hierarchy and Cash Holding: A Study From Pakistan
Bibliographic record
Abstract
This study aims to investigate the impact of the ownership hierarchy on the retention of cash. Ownership structure has dissimilar from each organization as some firms have surplus cash holdings and some firms have fewer cash holdings. This study examines the pattern of shareholding in Pakistan and its consequences on holding of cash. Trade-off theory, peaking-order theory, and agency theory have already hashed out the sensation between cash holding and ownership structure. Fixed Redundant likelihood test, Hausman Test and Panel Data Regression model are used to compute the final results. This study involves 74 non-financial firms to investigate the prime effects. The data have been taken from the biggest database of Karachi Stock Exchange (KSE) 100 index Pakistan and company financial reports from the period 2006 to 2017. Significant findings of this paper are based on two different research questions. First, how the pattern of shareholding has an impact on the decision of cash holding? Second, how the boundaries of the firm affect cash holding? These findings imply that cash holding and ownership structure are a vital element of the firm’s financial policy. This paper concludes that there is a negative and substantial relationship between cash holding and the pattern of shareholdings and the boundaries of the firm have an imperative effect on the holding of cash.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".